Overview

Dataset statistics

Number of variables16
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory456.5 KiB
Average record size in memory467.4 B

Variable types

Numeric7
Categorical9

Alerts

Sexo is highly imbalanced (50.7%)Imbalance
id has unique valuesUnique
SatServicioWifi has 36 (3.6%) zerosZeros
MinRetrasoSalida has 552 (55.2%) zerosZeros

Reproduction

Analysis started2024-03-08 04:19:52.079038
Analysis finished2024-03-08 04:19:57.063499
Duration4.98 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65500.67
Minimum126
Maximum129767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-07T23:19:57.452869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile6560
Q132161.5
median65798.5
Q398936.5
95-th percentile124360.15
Maximum129767
Range129641
Interquartile range (IQR)66775

Descriptive statistics

Standard deviation38003.404
Coefficient of variation (CV)0.58019871
Kurtosis-1.2185685
Mean65500.67
Median Absolute Deviation (MAD)33448.5
Skewness0.0029160529
Sum65500670
Variance1.4442587 × 109
MonotonicityNot monotonic
2024-03-07T23:19:57.570035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63296 1
 
0.1%
60974 1
 
0.1%
23422 1
 
0.1%
54385 1
 
0.1%
95626 1
 
0.1%
70893 1
 
0.1%
32681 1
 
0.1%
45162 1
 
0.1%
118425 1
 
0.1%
70047 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
126 1
0.1%
302 1
0.1%
346 1
0.1%
487 1
0.1%
548 1
0.1%
761 1
0.1%
797 1
0.1%
874 1
0.1%
1162 1
0.1%
1179 1
0.1%
ValueCountFrequency (%)
129767 1
0.1%
129765 1
0.1%
129483 1
0.1%
129372 1
0.1%
129356 1
0.1%
129310 1
0.1%
128934 1
0.1%
128899 1
0.1%
128868 1
0.1%
128845 1
0.1%

Sexo
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
M
492 
F
488 
 
14
Male
 
4
Female
 
2

Length

Max length6
Median length1
Mean length1.022
Min length1

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 492
49.2%
F 488
48.8%
14
 
1.4%
Male 4
 
0.4%
Female 2
 
0.2%

Length

2024-03-07T23:19:57.676637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:57.768224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
m 492
49.9%
f 488
49.5%
male 4
 
0.4%
female 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
M 496
48.5%
F 490
47.9%
14
 
1.4%
e 8
 
0.8%
a 6
 
0.6%
l 6
 
0.6%
m 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 986
96.5%
Lowercase Letter 22
 
2.2%
Space Separator 14
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8
36.4%
a 6
27.3%
l 6
27.3%
m 2
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
M 496
50.3%
F 490
49.7%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1008
98.6%
Common 14
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 496
49.2%
F 490
48.6%
e 8
 
0.8%
a 6
 
0.6%
l 6
 
0.6%
m 2
 
0.2%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 496
48.5%
F 490
47.9%
14
 
1.4%
e 8
 
0.8%
a 6
 
0.6%
l 6
 
0.6%
m 2
 
0.2%

TipoCliente
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
Frecuente
805 
Esporadico
195 

Length

Max length10
Median length9
Mean length9.195
Min length9

Characters and Unicode

Total characters9195
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrecuente
2nd rowFrecuente
3rd rowFrecuente
4th rowFrecuente
5th rowFrecuente

Common Values

ValueCountFrequency (%)
Frecuente 805
80.5%
Esporadico 195
 
19.5%

Length

2024-03-07T23:19:57.860814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:57.933383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
frecuente 805
80.5%
esporadico 195
 
19.5%

Most occurring characters

ValueCountFrequency (%)
e 2415
26.3%
r 1000
10.9%
c 1000
10.9%
F 805
 
8.8%
u 805
 
8.8%
n 805
 
8.8%
t 805
 
8.8%
o 390
 
4.2%
E 195
 
2.1%
s 195
 
2.1%
Other values (4) 780
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8195
89.1%
Uppercase Letter 1000
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2415
29.5%
r 1000
12.2%
c 1000
12.2%
u 805
 
9.8%
n 805
 
9.8%
t 805
 
9.8%
o 390
 
4.8%
s 195
 
2.4%
p 195
 
2.4%
a 195
 
2.4%
Other values (2) 390
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
F 805
80.5%
E 195
 
19.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 9195
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2415
26.3%
r 1000
10.9%
c 1000
10.9%
F 805
 
8.8%
u 805
 
8.8%
n 805
 
8.8%
t 805
 
8.8%
o 390
 
4.2%
E 195
 
2.1%
s 195
 
2.1%
Other values (4) 780
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2415
26.3%
r 1000
10.9%
c 1000
10.9%
F 805
 
8.8%
u 805
 
8.8%
n 805
 
8.8%
t 805
 
8.8%
o 390
 
4.2%
E 195
 
2.1%
s 195
 
2.1%
Other values (4) 780
 
8.5%

Edad
Real number (ℝ)

Distinct69
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.643
Minimum7
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-07T23:19:58.022465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q352
95-th percentile64
Maximum80
Range73
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.386953
Coefficient of variation (CV)0.38813795
Kurtosis-0.79715536
Mean39.643
Median Absolute Deviation (MAD)12
Skewness-0.050977284
Sum39643
Variance236.75831
MonotonicityNot monotonic
2024-03-07T23:19:58.135587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 29
 
2.9%
45 27
 
2.7%
44 26
 
2.6%
27 26
 
2.6%
39 25
 
2.5%
26 25
 
2.5%
47 25
 
2.5%
43 23
 
2.3%
33 23
 
2.3%
29 23
 
2.3%
Other values (59) 748
74.8%
ValueCountFrequency (%)
7 4
0.4%
8 9
0.9%
9 8
0.8%
10 9
0.9%
11 4
0.4%
12 8
0.8%
13 6
0.6%
14 5
0.5%
15 4
0.4%
16 9
0.9%
ValueCountFrequency (%)
80 2
 
0.2%
74 1
 
0.1%
73 1
 
0.1%
72 1
 
0.1%
71 1
 
0.1%
70 6
0.6%
69 6
0.6%
68 8
0.8%
67 5
0.5%
66 9
0.9%

TipoViaje
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Negocios
701 
Personal
299 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8000
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegocios
2nd rowPersonal
3rd rowNegocios
4th rowPersonal
5th rowNegocios

Common Values

ValueCountFrequency (%)
Negocios 701
70.1%
Personal 299
29.9%

Length

2024-03-07T23:19:58.240187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:58.316163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
negocios 701
70.1%
personal 299
29.9%

Most occurring characters

ValueCountFrequency (%)
o 1701
21.3%
e 1000
12.5%
s 1000
12.5%
N 701
8.8%
g 701
8.8%
c 701
8.8%
i 701
8.8%
P 299
 
3.7%
r 299
 
3.7%
n 299
 
3.7%
Other values (2) 598
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7000
87.5%
Uppercase Letter 1000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1701
24.3%
e 1000
14.3%
s 1000
14.3%
g 701
10.0%
c 701
10.0%
i 701
10.0%
r 299
 
4.3%
n 299
 
4.3%
a 299
 
4.3%
l 299
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
N 701
70.1%
P 299
29.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 8000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1701
21.3%
e 1000
12.5%
s 1000
12.5%
N 701
8.8%
g 701
8.8%
c 701
8.8%
i 701
8.8%
P 299
 
3.7%
r 299
 
3.7%
n 299
 
3.7%
Other values (2) 598
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1701
21.3%
e 1000
12.5%
s 1000
12.5%
N 701
8.8%
g 701
8.8%
c 701
8.8%
i 701
8.8%
P 299
 
3.7%
r 299
 
3.7%
n 299
 
3.7%
Other values (2) 598
 
7.5%

Clase
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size65.4 KiB
Ejecutiva
463 
Economica
448 
MuyEconomicanomica
89 

Length

Max length18
Median length9
Mean length9.801
Min length9

Characters and Unicode

Total characters9801
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEjecutiva
2nd rowEconomica
3rd rowEjecutiva
4th rowEconomica
5th rowEjecutiva

Common Values

ValueCountFrequency (%)
Ejecutiva 463
46.3%
Economica 448
44.8%
MuyEconomicanomica 89
 
8.9%

Length

2024-03-07T23:19:58.392735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:58.467307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
ejecutiva 463
46.3%
economica 448
44.8%
muyeconomicanomica 89
 
8.9%

Most occurring characters

ValueCountFrequency (%)
c 1626
16.6%
o 1163
11.9%
i 1089
11.1%
a 1089
11.1%
E 1000
10.2%
n 626
 
6.4%
m 626
 
6.4%
u 552
 
5.6%
j 463
 
4.7%
e 463
 
4.7%
Other values (4) 1104
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8712
88.9%
Uppercase Letter 1089
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 1626
18.7%
o 1163
13.3%
i 1089
12.5%
a 1089
12.5%
n 626
 
7.2%
m 626
 
7.2%
u 552
 
6.3%
j 463
 
5.3%
e 463
 
5.3%
t 463
 
5.3%
Other values (2) 552
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
E 1000
91.8%
M 89
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 9801
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 1626
16.6%
o 1163
11.9%
i 1089
11.1%
a 1089
11.1%
E 1000
10.2%
n 626
 
6.4%
m 626
 
6.4%
u 552
 
5.6%
j 463
 
4.7%
e 463
 
4.7%
Other values (4) 1104
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 1626
16.6%
o 1163
11.9%
i 1089
11.1%
a 1089
11.1%
E 1000
10.2%
n 626
 
6.4%
m 626
 
6.4%
u 552
 
5.6%
j 463
 
4.7%
e 463
 
4.7%
Other values (4) 1104
11.3%

DistanciaREconomicarrida
Real number (ℝ)

Distinct658
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1183.202
Minimum67
Maximum3995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-07T23:19:58.561896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile184
Q1412.5
median850.5
Q31739
95-th percentile3286.5
Maximum3995
Range3928
Interquartile range (IQR)1326.5

Descriptive statistics

Standard deviation996.90101
Coefficient of variation (CV)0.84254507
Kurtosis0.12683499
Mean1183.202
Median Absolute Deviation (MAD)522.5
Skewness1.0848061
Sum1183202
Variance993811.63
MonotonicityNot monotonic
2024-03-07T23:19:58.679508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2475 7
 
0.7%
296 7
 
0.7%
1671 6
 
0.6%
337 6
 
0.6%
192 6
 
0.6%
304 5
 
0.5%
201 5
 
0.5%
368 5
 
0.5%
328 5
 
0.5%
861 5
 
0.5%
Other values (648) 943
94.3%
ValueCountFrequency (%)
67 1
 
0.1%
77 2
 
0.2%
78 1
 
0.1%
86 2
 
0.2%
89 1
 
0.1%
95 2
 
0.2%
98 1
 
0.1%
101 1
 
0.1%
108 1
 
0.1%
109 5
0.5%
ValueCountFrequency (%)
3995 1
0.1%
3953 1
0.1%
3944 1
0.1%
3904 1
0.1%
3892 1
0.1%
3880 1
0.1%
3873 1
0.1%
3853 1
0.1%
3842 1
0.1%
3836 1
0.1%

SatServicioWifi
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.701
Minimum0
Maximum5
Zeros36
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-07T23:19:58.774098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3577343
Coefficient of variation (CV)0.50267838
Kurtosis-0.89008337
Mean2.701
Median Absolute Deviation (MAD)1
Skewness0.023902048
Sum2701
Variance1.8434424
MonotonicityNot monotonic
2024-03-07T23:19:58.852674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 245
24.5%
2 231
23.1%
4 192
19.2%
1 186
18.6%
5 110
11.0%
0 36
 
3.6%
ValueCountFrequency (%)
0 36
 
3.6%
1 186
18.6%
2 231
23.1%
3 245
24.5%
4 192
19.2%
5 110
11.0%
ValueCountFrequency (%)
5 110
11.0%
4 192
19.2%
3 245
24.5%
2 231
23.1%
1 186
18.6%
0 36
 
3.6%

SatPuntualidad
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
5
229 
4
220 
3
178 
2
160 
1
151 
Other values (2)
62 

Length

Max length6
Median length1
Mean length1.03
Min length1

Characters and Unicode

Total characters1030
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row1
4th row2
5th row5

Common Values

ValueCountFrequency (%)
5 229
22.9%
4 220
22.0%
3 178
17.8%
2 160
16.0%
1 151
15.1%
0 56
 
5.6%
Cuatro 6
 
0.6%

Length

2024-03-07T23:19:58.941257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:59.025335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
5 229
22.9%
4 220
22.0%
3 178
17.8%
2 160
16.0%
1 151
15.1%
0 56
 
5.6%
cuatro 6
 
0.6%

Most occurring characters

ValueCountFrequency (%)
5 229
22.2%
4 220
21.4%
3 178
17.3%
2 160
15.5%
1 151
14.7%
0 56
 
5.4%
C 6
 
0.6%
u 6
 
0.6%
a 6
 
0.6%
t 6
 
0.6%
Other values (2) 12
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 994
96.5%
Lowercase Letter 30
 
2.9%
Uppercase Letter 6
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 229
23.0%
4 220
22.1%
3 178
17.9%
2 160
16.1%
1 151
15.2%
0 56
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
u 6
20.0%
a 6
20.0%
t 6
20.0%
r 6
20.0%
o 6
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 994
96.5%
Latin 36
 
3.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 229
23.0%
4 220
22.1%
3 178
17.9%
2 160
16.1%
1 151
15.2%
0 56
 
5.6%
Latin
ValueCountFrequency (%)
C 6
16.7%
u 6
16.7%
a 6
16.7%
t 6
16.7%
r 6
16.7%
o 6
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 229
22.2%
4 220
21.4%
3 178
17.3%
2 160
15.5%
1 151
14.7%
0 56
 
5.4%
C 6
 
0.6%
u 6
 
0.6%
a 6
 
0.6%
t 6
 
0.6%
Other values (2) 12
 
1.2%

SatComidaBebidas
Real number (ℝ)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.965
Minimum0
Maximum300
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-07T23:19:59.114410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum300
Range300
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.971972
Coefficient of variation (CV)4.6267818
Kurtosis161.38866
Mean4.965
Median Absolute Deviation (MAD)1
Skewness12.747872
Sum4965
Variance527.71149
MonotonicityNot monotonic
2024-03-07T23:19:59.192986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 239
23.9%
3 216
21.6%
5 205
20.5%
2 203
20.3%
1 130
13.0%
300 6
 
0.6%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 130
13.0%
2 203
20.3%
3 216
21.6%
4 239
23.9%
5 205
20.5%
300 6
 
0.6%
ValueCountFrequency (%)
300 6
 
0.6%
5 205
20.5%
4 239
23.9%
3 216
21.6%
2 203
20.3%
1 130
13.0%
0 1
 
0.1%

ComodidadSilla
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
4
301 
5
247 
3
187 
2
148 
1
117 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row4
4th row5
5th row4

Common Values

ValueCountFrequency (%)
4 301
30.1%
5 247
24.7%
3 187
18.7%
2 148
14.8%
1 117
 
11.7%

Length

2024-03-07T23:19:59.281570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:59.366154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4 301
30.1%
5 247
24.7%
3 187
18.7%
2 148
14.8%
1 117
 
11.7%

Most occurring characters

ValueCountFrequency (%)
4 301
30.1%
5 247
24.7%
3 187
18.7%
2 148
14.8%
1 117
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 301
30.1%
5 247
24.7%
3 187
18.7%
2 148
14.8%
1 117
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 301
30.1%
5 247
24.7%
3 187
18.7%
2 148
14.8%
1 117
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 301
30.1%
5 247
24.7%
3 187
18.7%
2 148
14.8%
1 117
 
11.7%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
4
264 
5
241 
3
217 
2
159 
1
119 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4 264
26.4%
5 241
24.1%
3 217
21.7%
2 159
15.9%
1 119
11.9%

Length

2024-03-07T23:19:59.458742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:59.538318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4 264
26.4%
5 241
24.1%
3 217
21.7%
2 159
15.9%
1 119
11.9%

Most occurring characters

ValueCountFrequency (%)
4 264
26.4%
5 241
24.1%
3 217
21.7%
2 159
15.9%
1 119
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 264
26.4%
5 241
24.1%
3 217
21.7%
2 159
15.9%
1 119
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 264
26.4%
5 241
24.1%
3 217
21.7%
2 159
15.9%
1 119
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 264
26.4%
5 241
24.1%
3 217
21.7%
2 159
15.9%
1 119
11.9%

SatServicioAbordo
Real number (ℝ)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.334
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)0.5%
Memory size7.9 KiB
2024-03-07T23:19:59.612385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3145996
Coefficient of variation (CV)0.39430103
Kurtosis-0.53281225
Mean3.334
Median Absolute Deviation (MAD)1
Skewness-0.48438655
Sum3334
Variance1.7281722
MonotonicityNot monotonic
2024-03-07T23:19:59.692962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 282
28.2%
3 239
23.9%
5 220
22.0%
2 140
14.0%
1 114
11.4%
-1 5
 
0.5%
ValueCountFrequency (%)
-1 5
 
0.5%
1 114
11.4%
2 140
14.0%
3 239
23.9%
4 282
28.2%
5 220
22.0%
ValueCountFrequency (%)
5 220
22.0%
4 282
28.2%
3 239
23.9%
2 140
14.0%
1 114
11.4%
-1 5
 
0.5%

NivelLimpieza
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
4
252 
3
239 
5
212 
2
161 
1
136 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4 252
25.2%
3 239
23.9%
5 212
21.2%
2 161
16.1%
1 136
13.6%

Length

2024-03-07T23:19:59.780546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:19:59.859120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4 252
25.2%
3 239
23.9%
5 212
21.2%
2 161
16.1%
1 136
13.6%

Most occurring characters

ValueCountFrequency (%)
4 252
25.2%
3 239
23.9%
5 212
21.2%
2 161
16.1%
1 136
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 252
25.2%
3 239
23.9%
5 212
21.2%
2 161
16.1%
1 136
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 252
25.2%
3 239
23.9%
5 212
21.2%
2 161
16.1%
1 136
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 252
25.2%
3 239
23.9%
5 212
21.2%
2 161
16.1%
1 136
13.6%

MinRetrasoSalida
Real number (ℝ)

ZEROS 

Distinct113
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.082
Minimum0
Maximum794
Zeros552
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-07T23:19:59.960718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile76.05
Maximum794
Range794
Interquartile range (IQR)14

Descriptive statistics

Standard deviation44.380885
Coefficient of variation (CV)2.759662
Kurtosis105.1192
Mean16.082
Median Absolute Deviation (MAD)0
Skewness7.9069785
Sum16082
Variance1969.6629
MonotonicityNot monotonic
2024-03-07T23:20:00.088336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 552
55.2%
1 32
 
3.2%
6 25
 
2.5%
4 21
 
2.1%
3 21
 
2.1%
2 18
 
1.8%
10 16
 
1.6%
5 13
 
1.3%
7 11
 
1.1%
18 10
 
1.0%
Other values (103) 281
28.1%
ValueCountFrequency (%)
0 552
55.2%
1 32
 
3.2%
2 18
 
1.8%
3 21
 
2.1%
4 21
 
2.1%
5 13
 
1.3%
6 25
 
2.5%
7 11
 
1.1%
8 8
 
0.8%
9 10
 
1.0%
ValueCountFrequency (%)
794 1
0.1%
344 1
0.1%
302 1
0.1%
293 1
0.1%
283 1
0.1%
282 1
0.1%
237 1
0.1%
222 1
0.1%
219 1
0.1%
200 1
0.1%

satisfaccion
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
neutro o insatisfecho
569 
satisfecho
431 

Length

Max length21
Median length21
Mean length16.259
Min length10

Characters and Unicode

Total characters16259
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutro o insatisfecho
2nd rowneutro o insatisfecho
3rd rowsatisfecho
4th rowneutro o insatisfecho
5th rowsatisfecho

Common Values

ValueCountFrequency (%)
neutro o insatisfecho 569
56.9%
satisfecho 431
43.1%

Length

2024-03-07T23:20:00.196047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-07T23:20:00.271186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
neutro 569
26.6%
o 569
26.6%
insatisfecho 569
26.6%
satisfecho 431
20.2%

Most occurring characters

ValueCountFrequency (%)
o 2138
13.1%
s 2000
12.3%
e 1569
9.7%
t 1569
9.7%
i 1569
9.7%
n 1138
7.0%
1138
7.0%
a 1000
6.2%
f 1000
6.2%
c 1000
6.2%
Other values (3) 2138
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15121
93.0%
Space Separator 1138
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2138
14.1%
s 2000
13.2%
e 1569
10.4%
t 1569
10.4%
i 1569
10.4%
n 1138
7.5%
a 1000
6.6%
f 1000
6.6%
c 1000
6.6%
h 1000
6.6%
Other values (2) 1138
7.5%
Space Separator
ValueCountFrequency (%)
1138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15121
93.0%
Common 1138
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2138
14.1%
s 2000
13.2%
e 1569
10.4%
t 1569
10.4%
i 1569
10.4%
n 1138
7.5%
a 1000
6.6%
f 1000
6.6%
c 1000
6.6%
h 1000
6.6%
Other values (2) 1138
7.5%
Common
ValueCountFrequency (%)
1138
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2138
13.1%
s 2000
12.3%
e 1569
9.7%
t 1569
9.7%
i 1569
9.7%
n 1138
7.0%
1138
7.0%
a 1000
6.2%
f 1000
6.2%
c 1000
6.2%
Other values (3) 2138
13.1%

Interactions

2024-03-07T23:19:56.137124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.238193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.898497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.604017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.308710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.922099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.537256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:56.225204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.322271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.987082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.699749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.396294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.022696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.622334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:56.313789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.411858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.080479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.794911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.482678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.110280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.707915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:56.406377image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.514455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.194087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.898103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.573267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.200864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.797597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:56.488955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.604040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.281673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.001604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.656846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.282442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.877373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:56.575538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.693626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.374262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.102199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.747434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.364018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.959952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:56.663120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:52.805909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:53.493411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.209309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:54.830014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:55.448600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-07T23:19:56.048538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-07T23:19:56.796745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-07T23:19:56.983924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idSexoTipoClienteEdadTipoViajeClaseDistanciaREconomicarridaSatServicioWifiSatPuntualidadSatComidaBebidasComodidadSillaSatEntretenimientoSatServicioAbordoNivelLimpiezaMinRetrasoSalidasatisfaccion
063296MFrecuente16NegociosEjecutiva319232300333331neutro o insatisfecho
173453FFrecuente7PersonalEconomica1012355252526neutro o insatisfecho
21316FFrecuente39NegociosEjecutiva220411344430satisfecho
3128845FFrecuente9PersonalEconomica247542451550neutro o insatisfecho
432338MFrecuente56NegociosEjecutiva334405342250satisfecho
5113543MFrecuente37NegociosEjecutiva1954423344410neutro o insatisfecho
622065MFrecuente21NegociosEjecutiva317955555250satisfecho
747721FFrecuente51NegociosEjecutiva310733455530satisfecho
824019FFrecuente47NegociosEconomica42753315522satisfecho
997453MaleFrecuente52NegociosEjecutiva48422444430satisfecho
idSexoTipoClienteEdadTipoViajeClaseDistanciaREconomicarridaSatServicioWifiSatPuntualidadSatComidaBebidasComodidadSillaSatEntretenimientoSatServicioAbordoNivelLimpiezaMinRetrasoSalidasatisfaccion
99043790MFrecuente69PersonalMuyEconomicanomica54634222525neutro o insatisfecho
99153647MFrecuente27NegociosMuyEconomicanomica187400525550satisfecho
9925448Frecuente24NegociosEjecutiva180933333130neutro o insatisfecho
99375348MFrecuente50PersonalEconomica261525235430neutro o insatisfecho
994121338FFrecuente59NegociosEjecutiva320355452237satisfecho
99529336FFrecuente40PersonalEconomica93354111310satisfecho
99688494MEsporadico17NegociosEconomica25054222420satisfecho
99783526FFrecuente36PersonalEconomica94621111310neutro o insatisfecho
998126503Frecuente67PersonalEconomica178822222320neutro o insatisfecho
99995159MFrecuente28PersonalEconomica239255555531neutro o insatisfecho